Attain Peak Plant Performance

Optimize Every KPI

For process manufacturers the need to introduce new products and recipes using the same resources and production assets is increasing every day.

Production lines designed to be identical lead to different and variable yield and quality. Even with perfect recipes and SOPs there is large shift to shift variability. Getting products right the first time and achieving consistent outcomes is one of the biggest challenges.

AI process optimization software can overcome these challenges. But, until now has been accessible only to a few. It requires experts, dynamic process models and a lot of data. It is hard to achieve meaningful ROI with legacy optimization software.

Introducing a Breakthrough Technology: The Quartic Process Optimizer

Achieve ROI in 90 days or less, no process models, no big data required.

"With Quartic we have reduced batch to batch variability by 79%"
- Global Food and Beverage Company


  • Optimize any batch unit operation and build a multi-unit optimizer
  • Optimization results can be achieved in as little as 10 batch runs
  • Simple deployment results in accelerated ROI


  • Large amounts of historical data sets are not required
  • No first-principle process models or digital twins are needed
  • You can optimize single or multiple KPIs under any number of constraints

Additional Content

Achieve Successful Outcomes with AI for Process Optimization

In this webinar Russ Rhinehart, hall of fame Optimization expert, and Xiaozhou Wang, Chief Data Scientist of describe the shortcomings and challenges of legacy process optimization systems and how modern, AI based approaches can overcome them.

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Optimizing Continuous Manufacturing Processes

In this blog post, we will walk through one example of a continuous manufacturing process and demonstrate how advanced process control and machine learning optimization can play a key role in the future of continuous manufacturing.

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To achieve Agility in manufacturing – embrace Variability – Part 1

With supply chain disruptions and the increasing pace of new product introductions, variability has almost become a constant for process manufacturers. In this blog series, Rajiv Anand, founder, and CEO of discusses how to embrace variability to build an agile manufacturing enterprise.

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Optimization with Offline Reinforcement Learning

In this article, the research team discusses the results of two offline reinforcement learning approaches - by conservative Q learning and MOPO - inspired by Decision Transformer

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Download Process Optimizer application note

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